LearnClust (version 1.1)

divisiveHC.details: To explain the divisive hierarchical clusterization algorithm by distance and approach.

Description

To explain the complete divisive hierarchical clusterization algorithm by choosing distance and approach types.

Usage

divisiveHC.details(data, distance, approach)

Arguments

data

could be a numeric vector, a matrix or a numeric data frame. It will be transformed into matrix and list to be used.

distance

is a string. It chooses the distance to use.

approach

is a string. It chooses the approach to use.

Value

A list with the divided clusters. Explanation

Details

This function is the main part of the divisive hierarchical clusterization method. It explains the theoretical algorithm step by step.

1 - The function transforms data in useful object to be used.

2 - It creates a cluster that includes every simple elements.

3 - It initializes posible clusters using the initial elements.

4 - It calculates a matrix distance with the clusters created in the 3rd step.

5 - It chooses the maximal distance value and gets the clusters to be divided.

6 - It divides the cluster into two new complementary clusters and updates the clusters list.

6 - It repeats these steps until every cluster can't be divided again. The solution includes every simple cluster.

Examples

# NOT RUN {
a <- c(1,2,1,3,1,4,1,5,1,6)

matrixA <- matrix(a,ncol=2)

dataFrameA <- data.frame(matrixA)

divisiveHC.details(a,'EUC','MAX')

divisiveHC.details(matrixA,'MAN','AVG')

divisiveHC.details(dataFrameA,'CHE','MIN')

# }